Principal texture direction based block level image reordering and use of color edge features for application of object based image retrieval

Abstract

In this paper, the authors have presented a novel content-based image retrieval (CBIR) scheme based on the combination of color, shape, and texture visual image features. Initially, the combined features of color and shape are derived from the object region of an image using the proposed color edge map approach. This approach is suitable to extract both the color and shape based features simultaneously from image object region. We have preserved more information associated with the object region and some significant information from the background region for enabling better retrieval efficiency. In the subsequent stage, we have extracted texture features from the preprocessed image. This preprocessed image is obtained after decomposition of an image into non-overlapping blocks followed by reordering all blocks based on their principal texture direction. The notion supports the variation present on image data can be controlled by rearranging each block as per their principal direction and some texture based parameters derived from the preprocessed image. The final feature vector consists of color, shape, and texture-related features in their correct proportions. Proposed CBIR scheme is extensively tested using four coral image databases (i.e. 1,000 color images from 10 different classes, 10,000 color images from 20 different classes, 7,200 images from 100 different classes and 17,125 images from 20 different classes). Experimental results show that the proposed CBIR scheme has better retrieval efficiency in terms of precision and recall than other related schemes.